Candidate Bio:

Xue is a postdoc in Northwestern University. Before that, he obtained PHD at University of Texas at Austin, advised by Prof. David Zuckerman. He is broadly interested in randomized algorithms and derandomization. Specific areas include algorithms for big data — sparse recovery and fast Fourier transform, foundations of machine learning, and pseudorandomness.

Keywords: Algorithms for big data, foundations of machine learning, pseudorandomness, computational complexity

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Candidate Bio:

“I have completed my bachelor and masters degrees in Mathematics and Computer Science in Tel Aviv University when I was an high school student.
After high-school, I have enlisted to a research unit in the army for three years (as military service is mandatory in Israel).
Simultaneously, I was working on my PhD under the advising of Uri Zwick and Haim Kaplan.
I am usually interested in classical graph algorithms, data structures and graph theory.

I am currently 23 years old and expect finishing the PhD by the end of the current academic year.”

Keywords: algorithms, data structures, graph theory, graph algorithms, combinatorics

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Candidate Bio:

Sumegha Garg is a graduate student in Computer Science at Princeton University, where she is advised by Professor Mark Braverman. Before coming to Princeton, she received her Bachelor’s degree in Computer Science and Engineering from Indian Institute of Technology, Delhi. She is broadly interested in theoretical computer science, with a focus on complexity theory and algorithmic fairness. The primary aim of her research has been studying the computation limits of and the role of randomness in space-bounded computational models. Another aim of her research has been understanding the sources of unfairness in machine learning algorithms.

Keywords: Computational complexity, algorithmic fairness, space-bounded computation, randomness in computing

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Sivakanth Gopi (called “Gopi” by friends and colleagues) is a postdocotoral researcher in the Algorithms group at Microsoft Research Redmond. He received a PhD in Computer Science from Princeton University in 2018, advised by Zeev Dvir; the title of his dissertation was “Locality in coding theory”. His main research interests are in coding theory and its connections to pseudorandomness, complexity theory and cryptography. He is also involved with the DNA Storage project, erasure coding in Azure storage and Project Laplace (differential privacy) at Microsoft. He enjoys spending leisure time learning new things, staying fit, exploring nature and talking to his family.

Keywords: Coding theory, locally decodable codes, complexity theory, pseudorandomness

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Ariel Schvartzman Cohenca is a PhD candidate at Princeton University advised by S. Matthew Weinberg. Ariel’s work focuses in understanding the trade-off between optimality and simplicity in the design of multi-dimensional auctions. He was awarded the Department of Computer Science’s Graduate Student Teaching Award in 2017, and the School of Engineering and Applied Science’s Award for Excellence in 2018. During the summer of 2018, Ariel was a research intern at Google-Mountain View. He obtained his B.S. in Mathematics with Computer Science from MIT in 2015.

Keywords: economics and computation, algorithmic game theory

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I am a fifth (final) year PHD student in the Department of Computer Science and Engineering at The State University of New York (SUNY) at Buffalo under supervision of Dr. Jinhui Xu . Before that I got my Master degree in Mathematics at University of Western Ontario in 2015, and I got my Bachelor degree in Mathematics and Applied Mathematics at Shandong University in 2014.

Keywords: Differential Privacy, Learning Theory

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Candidate Bio:

I am a PhD candidate in CSAIL, MIT, advised by Piotr Indyk. During my PhD I completed summer internships in Microsoft Research Redmond (2019), Amazon Core Machine Learning Group (2017), and VMware Research Group (2015). I received my MSc from the Weizmann Institute, and BSc in Computer Science and Mathematics from the Technion, Israel.

Keywords: metric spaces, sketching, quantization, nearest neighbor search, dimension reduction

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Candidate Bio:

I am a 5th year graduate student at Rutgers University, and my advisor is Swastik Kopparty. My interests lie broadly in Discrete mathematics, probability, combinatorics, and coding theory. Before Rutgers, I received my master’s degree in Computer Science from Chennai Mathematical Institute.

Keywords: Discrete mathematics, Probability, Combinatorics, Coding Theory

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Tongyang Li is a Ph.D. candidate at the Department of Computer Science, University of Maryland. He received B.E. from Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from Department of Mathematical Sciences, Tsinghua University, both in 2015; he also received a Master degree from Department of Computer Science, University of Maryland in 2018. He is a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship. His research focuses on designing quantum algorithms for machine learning and optimization.

Keywords: Quantum computing, theoretical machine learning, optimization theory

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Candidate Bio:

Tanmay Inamdar is a fifth year PhD candidate at the University of Iowa, advised by Prof. Kasturi Varadarajan. His research is mainly focused on approximation algorithms. He is also interested in computational geometry and distributed algorithms.

Keywords: Approximation Algorithms, Computational Geometry, Parallel and Distributed Computing

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